Similarly, Luo, et.al., [1]} exploited both spectral and spatial information contained in HSI by combining CNN with parallel GRU based RNN which simplifies the training of GRU and improves performance. Bidirectional Convolutional LSTM (CLSTM) was proposed in [2]} to jointly exploit spectral-spatial feature of HSI for classification. In, [3]} combined multiscale local spectral-spatial features extracted by \(3D\) -CNN with a hierarchical RNN which learns the spatial dependencies of local spectral-spatial features at multiple scales. Recurrent \(2D\) -CNN and recurrent \(3D\) -CNN for HSIC was proposed in [4]} and along with an interesting comparison of these frameworks with their corresponding \(2D\)  and \(3D\) -CNN models, which validates the superiority of recurrent CNN. The work [5]} integrated CNN with CLSTM in which a \(3D\) -CNN model is used to capture low-level spectral-spatial features and CLSTM recurrently analyzes this low-level spectral-spatial information. Recently, [6]}, introduced a cascade RNN for HSIC which consist of two layers of GRU-based RNN, the first layer is used to reduce the redundant spectral bands and the second layer is used to learn the features from HSI, furthermore, few convolutional layers are employed to incorporate the rich spatial information contained in HSI.
